Compressed sensing (CS) provides an elegant framework for recovering sparse
signals from compressed measurements. For example, CS can exploit the structure
of natural images and recover an image from only a few random measurements. CS
is flexible and data efficient, but its application has been restricted by the
strong assumption of sparsity and costly reconstruction process. A recent
approach that combines CS with neural network generators has removed the
constraint of sparsity, but reconstruction remains slow. Here we propose a
novel framework that significantly improves both the performance and speed of
signal recovery by jointly training a generator and the optimisation process
for reconstruction via meta-learning. We explore training the measurements with
different objectives, and derive a family of models based on minimising
measurement errors. We show that Generative Adversarial Nets (GANs) can be
viewed as a special case in this family of models. Borrowing insights from the
CS perspective, we develop a novel way of improving GANs using gradient
information from the discriminator.

本文提出了一种结合压缩感知（CS）和神经网络生成器的新型框架，通过元学习联合训练生成器和优化过程，大大改善了信号恢复的速度和性能，并针对不同目标训练测量并从 CS 视角提出提高生成对抗网络（GANs）的新方法。